Abstract

In this paper, we present a spiking neural model of life span
inference. Through this model, we explore the biological plausibility of
performing Bayesian computations in the brain. Specifically, we address the issue
of representing probability distributions using neural circuits and combining
them in meaningful ways to perform inference. We show that applying these
methods to the life span inference task matches human performance on this task
better than an ideal Bayesian model due to the use of neuron tuning curves. We
also describe potential ways in which humans might be generating the priors
needed for this inference. This provides an initial step towards better
understanding how Bayesian computations may be implemented in a biologically
plausible neural network.